Dimensions des graphiques
Les dimensions des graphiques
Avec une dimension (une variable), on peut déjà faire un graphique
On peut également adapter la géométrie en fonction de ce qu’on veut montrer
On retrouve généralement, au moins, les dimensions cartésiennes (x et y)
On peut ajouter des dimensions en jouant sur d’autres variables visuelles
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
pl_22_23 |>
filter(! is.na(HomeGoals)) |>
group_by(HomeGoals, AwayGoals) |>
summarise(freq = n()) |>
mutate(rslt = case_when(
HomeGoals > AwayGoals ~ "victoire",
HomeGoals < AwayGoals ~ "défaite",
HomeGoals == AwayGoals ~ "nul")) |>
ggplot() +
geom_point(aes(HomeGoals, AwayGoals, size = freq, color = rslt)) +
coord_fixed(xlim = c(0,6),
ylim = c(0,6)) +
scale_color_manual(values = couleurs_rslt) +
labs(title = "Scores des matchs de Premier League") +
theme(plot.title = element_text(family = "Premier League"))
# Préparation des fréquences et résultats
freq_scores_rslt <- pl_22_23 |>
filter(! is.na(HomeGoals)) |>
group_by(HomeGoals, AwayGoals) |>
summarise(freq = n()) |>
mutate(rslt = case_when(
HomeGoals > AwayGoals ~ "victoire",
HomeGoals < AwayGoals ~ "défaite",
HomeGoals == AwayGoals ~ "nul"))
# Calcul des infos supplémentaires
nb_matchs <- sum(freq_scores_rslt$freq)
nb_vict <- sum(freq_scores_rslt[freq_scores_rslt$rslt == "victoire", "freq"])
nb_def <- sum(freq_scores_rslt[freq_scores_rslt$rslt == "défaite", "freq"])
nb_nul <- sum(freq_scores_rslt[freq_scores_rslt$rslt == "nul", "freq"])
# Couleurs des rslts
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
freq_scores_rslt |>
ggplot() +
geom_segment(x = 0, y = 0, xend = 6, yend = 6, linewidth = 0.5, color = "grey85") +
geom_point(aes(HomeGoals, AwayGoals, size = freq, color = rslt)) +
geom_curve(aes(x = 4.55, y = 3.25, xend = 4.1, yend = 2.1),
arrow = arrow(length = unit(0.12, "inch")), size = 0.5,
color = "gray40", curvature = -0.2) +
annotate("text", x = 4.55, y = 3.4, label = "3 matchs ont fini à 4-2", size = 4, color = "gray40", hjust = 0.5) +
coord_fixed(xlim = c(0.5, 5.5),
ylim = c(0.5, 5.5)) +
scale_x_discrete(limits = c(0:6)) +
scale_y_discrete(limits = c(0:6)) +
scale_color_manual(values = couleurs_rslt) +
scale_size_area(breaks = c(max(freq_scores_rslt$freq), 21, 10, 5)) +
labs(title = "Scores des matchs de Premier League",
subtitle = str_glue("Saison 22/23 - {nb_matchs} matchs: ",
"<span style = 'color:#00985f;'>{nb_vict} victoires</span> et ",
"<span style = 'color:#e55e5b;'>{nb_def} défaites</span> ",
"à domicile, ",
"<span style = 'color:#7b8288;'>{nb_nul} nuls</span>"),
x = "Buts marqués à domicile",
y = "Buts encaissés à domicile",
size = "Matchs",
caption = "Données FBref.com | 2 matchs non affichés (9-0, 7-0)") +
theme_minimal() +
theme(text = element_text(family = "Century Gothic"),
plot.title = element_text(family = "Premier League"),
plot.subtitle = element_markdown(),
panel.grid.major = element_line(color = "gray95"),
legend.position = c(0.75, 0.77),
legend.background = element_rect(fill = alpha("white", 0.8), linetype = "blank"),
axis.title.x = element_text(margin = margin(10, 0, 0, 0)),
axis.title.y = element_text(margin = margin(0, 10, 0, 0)),
plot.caption = element_text(color = "gray65", margin = margin(10,0,0,0))) +
guides(color="none")
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
leagues_22_23 <- rbind(pl_22_23, liga_22_23, seriea_22_23)
leagues_22_23 |>
group_by(Competition_Name, HomeGoals, AwayGoals) |>
summarise(freq = n()) |>
mutate(rslt = case_when(
HomeGoals > AwayGoals ~ "victoire",
HomeGoals < AwayGoals ~ "défaite",
HomeGoals == AwayGoals ~ "nul")) |>
ggplot() +
facet_wrap(~ Competition_Name, nrow = 1) +
geom_point(aes(HomeGoals, AwayGoals, size = freq, color = rslt)) +
coord_fixed(xlim = c(0.3, 5.5),
ylim = c(0.3, 5.5)) +
scale_x_discrete(limits = c(0:6)) +
scale_y_discrete(limits = c(0:6)) +
scale_color_manual(values = couleurs_rslt) +
labs(title = "Scores des matchs de championnats européens",
subtitle = "Saison 2022/23",
x = "Buts marqués à domicile",
y = "Buts encaissés à domicile") +
theme_minimal(base_size = 15) +
theme(legend.position = "none",
strip.text.x = element_text(face = "bold"),
text = element_text(family = "Century Gothic"),
axis.title.x = element_text(margin = margin(10, 0, 0, 0)),
axis.title.y = element_text(margin = margin(0, 10, 0, 0)),
axis.title = element_text(size = 10),
panel.spacing.x = unit(2, "lines"))
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
pl_20_23 <- rbind(pl_22_23, pl_21_22, pl_20_21)
pl_20_23 |>
group_by(Season_End_Year, HomeGoals, AwayGoals) |>
summarise(freq = n()) |>
mutate(rslt = case_when(
HomeGoals > AwayGoals ~ "victoire",
HomeGoals < AwayGoals ~ "défaite",
HomeGoals == AwayGoals ~ "nul")) |>
ggplot() +
facet_wrap(~ Season_End_Year) +
geom_point(aes(HomeGoals, AwayGoals, size = freq, color = rslt)) +
coord_fixed(xlim = c(0.3, 5.5),
ylim = c(0.3, 5.5)) +
scale_x_discrete(limits = c(0:6)) +
scale_y_discrete(limits = c(0:6)) +
scale_color_manual(values = couleurs_rslt) +
labs(title = "Scores des matchs de Premier League",
subtitle = "Saisons 2020/21 à 2022/23",
x = "Buts marqués à domicile",
y = "Buts encaissés à domicile") +
theme_minimal(base_size = 15) +
theme(legend.position = "none",
strip.text.x = element_text(face = "bold"),
text = element_text(family = "Century Gothic"),
title = element_text(family = "Premier League"),
axis.title.x = element_text(margin = margin(10, 0, 0, 0)),
axis.title.y = element_text(margin = margin(0, 10, 0, 0)),
panel.spacing = unit(2, "lines"))
rslt_equipes_pl <- data.frame(goal_for = numeric(), goal_against = numeric(), n = numeric(), team = numeric())
for (team in unique(pl_22_23$Home)){
freq_rslt_team <- pl_22_23 |>
filter((Home == team | Away == team) & ! is.na(HomeGoals)) |>
select(Home, HomeGoals, Away, AwayGoals) |>
mutate(goal_for = if_else(Home == team, HomeGoals, AwayGoals),
goal_against = if_else(Home == team, AwayGoals, HomeGoals)) |>
group_by(goal_for, goal_against) |>
summarise(n = n()) |>
mutate(team = team)
rslt_equipes_pl <- rbind(rslt_equipes_pl, freq_rslt_team)
}
classement_pl <- fb_season_team_stats("ENG", "M", 2023, "1st", "league_table")
ordre_equipes <- classement_pl |>
arrange(Rk) |>
pull(Squad)
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
rslt_equipes_pl |>
mutate(bp = goal_for, bc = goal_against, rslt = case_when(bp > bc ~ "victoire",
bp < bc ~ "défaite",
.default = "nul")) |>
dplyr::filter(team %in% ordre_equipes[c(1,2,3,4,10,11,12,18,19,20)]) |>
ggplot() +
facet_wrap(~ factor(team, levels = ordre_equipes), nrow = 2) +
geom_segment(x = 0, y = 0, xend = 6, yend = 6, linewidth = 0.4, color = "grey90") +
geom_point(aes(bp, bc, size = n, color = rslt)) +
scale_x_continuous(limits = c(0, 6), breaks = c(0:6)) +
scale_y_continuous(limits = c(0, 6), breaks = c(0:6)) +
scale_color_manual(values = couleurs_rslt) +
coord_fixed(xlim = c(0, 5.8),
ylim = c(0, 5.8), clip = "off") +
scale_size_area(max_size = 4) +
labs(title = "Scores d'équipes de Premier League",
subtitle = "Équipes classées 1, 2, 3, 4, 10 | 11, 12, 18, 19, 20",
x = "Buts marqués",
y = "Buts encaissés",
size = "Nombre \nde matchs") +
theme_minimal(base_size = 15) +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "gray95"),
text = element_text(family = "Century Gothic"),
plot.title = element_text(color = "gray35"),
plot.subtitle = element_text(color = "gray65", size = 12),
axis.title = element_text(size = 6),
strip.text = element_text(size = 9, family = "Premier League", colour = "gray40"),
axis.text = element_text(size = 6),
axis.title.x = element_text(margin = margin(8, 0, 0, 0)),
axis.title.y = element_text(margin = margin(0, 5, 0, 0)),
axis.text.x = element_text(margin = margin(-4,0,0,0)),
panel.spacing.x = unit(1.5, "lines"),
legend.position = "none") +
guides(color="none")
rslt_equipes_pl_20_23 <- data.frame(goal_for = numeric(), goal_against = numeric(), n = numeric(), team = numeric())
for (team in unique(pl_22_23$Home)){
freq_rslt_team <- pl_20_23 |>
filter((Home == team | Away == team) & ! is.na(HomeGoals)) |>
select(Season_End_Year, Home, HomeGoals, Away, AwayGoals) |>
mutate(goal_for = if_else(Home == team, HomeGoals, AwayGoals),
goal_against = if_else(Home == team, AwayGoals, HomeGoals)) |>
group_by(Season_End_Year, goal_for, goal_against) |>
summarise(n = n()) |>
mutate(team = team,
season = Season_End_Year)
rslt_equipes_pl_20_23 <- rbind(rslt_equipes_pl_20_23, freq_rslt_team)
}
couleurs_rslt <- c("victoire" = "#00985f", "défaite" = "#e55e5b", "nul" = "#7b8288")
rslt_equipes_pl_20_23 |>
mutate(bp = goal_for, bc = goal_against, rslt = case_when(bp > bc ~ "victoire",
bp < bc ~ "défaite",
.default = "nul")) |>
dplyr::filter(team %in% c("Newcastle Utd", "Brighton") & season %in% c(2021, 2022, 2023)) |>
ggplot() +
facet_grid(team ~ season) +
geom_segment(x = 0, y = 0, xend = 6, yend = 6, linewidth = 0.4, color = "grey90") +
geom_point(aes(bp, bc, size = n, color = rslt)) +
scale_x_continuous(limits = c(0, 6), breaks = c(0:6)) +
scale_y_continuous(limits = c(0, 6), breaks = c(0:6)) +
scale_color_manual(values = couleurs_rslt) +
coord_fixed(xlim = c(0, 5.8),
ylim = c(0, 5.8), clip = "off") +
scale_size_area(max_size = 4) +
labs(title = "Évolution des résultats de Newcastle Utd et Brighton",
x = "Buts marqués",
y = "Buts encaissés",
size = "Nombre \nde matchs") +
theme_minimal(base_size = 15) +
theme(panel.grid.minor = element_blank(),
panel.grid.major = element_line(color = "gray95"),
text = element_text(family = "Century Gothic"),
plot.title = element_text(color = "gray25", size = 14),
axis.title = element_text(size = 6),
strip.text = element_text(size = 9, family = "Premier League", colour = "gray40"),
axis.text = element_text(size = 6),
panel.spacing = unit(1, "lines"),
legend.position = "none") +
guides(color="none")Exemple d’amélioration
Avec R, il est possible d’obtenir le même résultat
On préfère des petits multiples pour comparer efficacement
Également en valeurs relatives
